
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "169"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 138 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 133 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	88.212287	105.063007	inf	inf	20.766900	34.468015	52.664712	97.400025	nan	nan	nan	0.000000	0.000000
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.651210	3.734172	24.006460	21.034740	16.666818	19.354504	7.675949	5.887126	0.6708	0.6703	0.3082	7.157828	5.377017
2459592	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.338019	3.768085	55.899883	55.671541	10.445564	12.653222	-1.712826	0.424625	0.7578	0.7774	0.4000	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.824040	5.409365	27.692072	24.806641	14.375127	16.437104	-2.037258	-1.702247	0.6396	0.6527	0.3925	4.918038	4.248071
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	357.298605	361.419590	inf	inf	31.352232	37.210704	177.749371	234.383597	nan	nan	nan	0.000000	0.000000
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	336.764293	337.926862	inf	inf	21.072261	46.319429	101.864764	334.925087	nan	nan	nan	0.000000	0.000000
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	211.827217	222.558964	inf	inf	22.329102	26.301224	50.707652	17.108956	nan	nan	nan	0.000000	0.000000
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	7.407019	5.943007	39.692962	35.157583	2.477807	3.251534	-2.200847	-0.778932	nan	nan	nan	9.985577	8.589727
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.668439	1.421814	3.122545	2.500766	6.110552	6.266863	-1.511311	-0.946174	0.6430	0.6557	0.3727	5.602654	5.036431
2459585	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	35.367969	36.147894	23.702294	23.167024	7.344600	7.742419	-2.835135	-1.036202	0.7134	0.7252	0.3102	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.063544	17.558722	39.917900	36.393733	10.666467	11.618134	34.076469	33.990502	0.5995	0.6152	0.3883	6.140683	4.785253
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.011141	4.775616	11.522346	10.483150	10.929558	11.930834	-1.885555	-0.989304	0.6377	0.6479	0.4113	5.530629	3.656875
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.569407	4.468749	10.556537	9.306560	13.291141	14.880097	-1.914720	-1.631023	0.6462	0.6575	0.4046	7.485184	5.278973
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.867939	6.758120	10.933745	10.466615	10.570126	12.047761	-1.774137	-1.451971	0.6437	0.6569	0.4052	4.353127	3.431916
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.170275	4.981793	12.071618	10.884575	12.108500	12.837234	-2.098682	-1.243150	0.6481	0.6614	0.3997	9.889635	7.411835
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.985541	4.927196	13.826349	12.294008	13.355812	14.903831	-2.422036	-2.241907	0.6436	0.6534	0.4051	15.359784	11.225714
2459578	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.924411	4.558845	31.044224	31.265444	9.387049	16.186849	1.347291	6.847540	0.7682	0.7926	0.4091	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.394065	6.349874	10.320720	9.626441	11.828354	13.239202	-1.874353	-1.242027	0.6429	0.6574	0.4023	7.897084	6.185511
2459576	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.721776	4.870024	7.764622	6.999944	11.029487	11.930064	-1.760369	-1.727259	0.6607	0.6733	0.4099	4.713235	3.848591
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.413912	3.799035	8.343230	7.460496	9.207163	9.049016	5.957237	4.847773	0.7207	0.7285	0.3605	6.453346	4.455995
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.577083	5.687178	14.929393	13.804071	13.751988	15.040577	-2.221246	-1.817491	0.6633	0.6756	0.4060	5.652468	3.396577
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.234328	5.242388	7.808276	7.098782	9.031125	9.714540	-2.578519	-2.627175	0.6673	0.6793	0.4027	8.124287	6.004993
2459571	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.162269	4.074271	32.069467	32.219476	30.409751	40.535654	1.634468	5.006484	0.7020	0.7254	0.3883	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.046105	5.164019	10.637490	9.903540	10.740620	11.738642	-1.771132	-1.189481	0.6647	0.6762	0.4149	4.832988	3.465210
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.751415	0.751415	0.674491	0.674491	0.881824	0.881824	0.311291	0.311291	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	2.930524	1.902742	2.224967	1.423725	-0.325241	-0.643216	-1.753189	-1.281368	0.6718	0.6800	0.4159	2.051237	1.746879
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.618482	5.714158	22.503877	21.098907	11.391417	12.762249	-1.783579	-1.348122	0.6731	0.6813	0.4084	4.490917	3.482702
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	4.86%	1.834857	0.967824	0.659321	0.227884	-0.775365	-1.198538	-1.406077	-1.092940	0.6745	0.6882	0.4054	2.076961	1.902209
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	13.51%	1.834323	1.264750	0.800492	0.614196	-0.321248	-0.233725	-1.637121	-1.483532	0.6751	0.6844	0.4145	2.006384	1.772338
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	1.08%	2.560178	1.968287	1.379623	1.102374	0.033078	0.103142	-1.623359	-1.424079	0.6726	0.6881	0.4051	1.960606	1.776936
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.800269	5.883522	12.185227	10.958427	12.329846	12.447386	-2.061453	-1.758223	0.6781	0.6859	0.4086	14.196876	10.889130
2459557	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.8184	0.8301	0.3142	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6718	0.6694	0.3997	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6681	0.6647	0.3963	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.314810	7.100227	16.537753	15.664351	17.267554	17.446370	0.091530	0.295318	0.6762	0.6867	0.4144	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	2.062324	1.492025	1.160531	0.855089	-0.695649	-0.660551	-2.008535	-1.856483	0.6736	0.6881	0.4097	1.917450	1.721288
2459550	digital_ok	-	28.01%	28.01%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.5423	0.5587	0.2670	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.459984	5.701004	12.225471	11.549250	11.964043	12.327090	-2.445280	-2.091078	0.6744	0.6874	0.4187	4.535137	3.465600
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.191288	5.980884	24.239369	23.334877	0.336382	0.935773	-2.199807	-1.716775	0.7417	0.7464	0.3629	13.858909	11.056833
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.662909	8.730951	20.038316	19.092779	18.230471	18.477889	-3.840336	-3.054317	0.6872	0.7035	0.4479	0.000000	0.000000
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	98.21%	1.79%	2.454912	1.576213	2.052912	1.752495	0.277711	0.482642	-1.424601	-1.610197	0.7666	0.7569	0.3845	0.992761	1.001694
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	78.57%	1.969634	1.177338	1.144607	0.866254	-0.950279	-0.159439	-1.545020	-1.435141	0.7554	0.7438	0.3795	30.651904	17.550883
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.412131	6.166485	12.616398	11.747428	10.268986	11.432684	-2.820180	-2.222900	0.6762	0.6945	0.4396	0.000000	0.000000
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.644618	7.216571	16.509144	15.072511	12.998130	14.221305	-2.373037	-2.171680	0.6850	0.7017	0.4275	38.844173	22.559623
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	98.82%	1.18%	1.974416	1.356832	1.017069	0.726176	-0.427390	-0.300616	-2.032863	-1.158374	0.7026	0.7142	0.4239	0.812255	0.816451
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.949191	5.312832	30.372969	30.684626	9.250943	21.821345	1.900790	7.113532	0.8628	0.9082	0.2582	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.248234	-0.711474	0.934009	-1.103056	-0.180895	-1.078654	0.524731	-0.868044	0.9136	0.9575	0.1809	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.114201	-0.291185	1.848157	-0.887275	-1.351758	-0.546715	-1.450160	-0.164145	0.8927	0.8633	0.1933	5.208738	4.533204
2459503	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.449991	0.134733	1.983051	-0.335065	2.945403	-0.407756	-1.271759	-0.584161	0.0599	0.0616	0.0040	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 169 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 169, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 169: 2459665

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶